论文标题
Newtonianvae：从像素通过物理潜在空间从像素的比例控制和目标识别
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
论文作者
论文摘要
学习低维的潜在状态空间动态模型已成为实现基于视觉的计划和控制的强大范式。我们引入了一个潜在的动态学习框架，该框架的设计独特，旨在诱导潜在空间中的比例可控制性，从而使使用比先前的工作更简单的控制器。我们表明，我们学到的动态模型可以从像素中进行比例控制，从而极大地简化和加速了基于视觉控制器的行为克隆，并在应用于模仿从演示中的开关控制器的模仿学习时提供了可解释的目标发现。
Learning low-dimensional latent state space dynamics models has been a powerful paradigm for enabling vision-based planning and learning for control. We introduce a latent dynamics learning framework that is uniquely designed to induce proportional controlability in the latent space, thus enabling the use of much simpler controllers than prior work. We show that our learned dynamics model enables proportional control from pixels, dramatically simplifies and accelerates behavioural cloning of vision-based controllers, and provides interpretable goal discovery when applied to imitation learning of switching controllers from demonstration.
